代码拉取完成,页面将自动刷新
import argparse
import functools
import time
import wave
from ppasr.predict import PPASRPredictor
from ppasr.utils.utils import add_arguments, print_arguments
parser = argparse.ArgumentParser(description=__doc__)
add_arg = functools.partial(add_arguments, argparser=parser)
add_arg('configs', str, 'configs/conformer.yml', "配置文件")
add_arg('wav_path', str, 'dataset/test.wav', "预测音频的路径")
add_arg('is_long_audio', bool, False, "是否为长语音")
add_arg('real_time_demo', bool, False, "是否使用实时语音识别演示")
add_arg('use_gpu', bool, True, "是否使用GPU预测")
add_arg('use_pun', bool, False, "是否给识别结果加标点符号")
add_arg('is_itn', bool, False, "是否对文本进行反标准化")
add_arg('pun_model_dir', str, 'models/pun_models/', "加标点符号的模型文件夹路径")
add_arg('model_path', str, 'models/conformer_streaming_fbank/infer', "导出的预测模型文件路径")
args = parser.parse_args()
print_arguments(args=args)
# 获取识别器
predictor = PPASRPredictor(configs=args.configs,
model_path=args.model_path,
use_gpu=args.use_gpu,
use_pun=args.use_pun,
pun_model_dir=args.pun_model_dir)
# 长语音识别
def predict_long_audio():
start = time.time()
result = predictor.predict_long(audio_data=args.wav_path, use_pun=args.use_pun, is_itn=args.is_itn)
score, text = result['score'], result['text']
print(f"长语音识别结果,消耗时间:{int(round((time.time() - start) * 1000))}, 识别结果: {text}, 得分: {score}")
# 短语音识别
def predict_audio():
start = time.time()
result = predictor.predict(audio_data=args.wav_path, use_pun=args.use_pun, is_itn=args.is_itn)
score, text = result['score'], result['text']
print(f"消耗时间:{int(round((time.time() - start) * 1000))}ms, 识别结果: {text}, 得分: {int(score)}")
# 实时识别模拟
def real_time_predict_demo():
# 识别间隔时间
interval_time = 0.5
CHUNK = int(16000 * interval_time)
# 读取数据
wf = wave.open(args.wav_path, 'rb')
data = wf.readframes(CHUNK)
# 播放
while data != b'':
start = time.time()
d = wf.readframes(CHUNK)
result = predictor.predict_stream(audio_data=data, use_pun=args.use_pun, is_itn=args.is_itn, is_end=d == b'')
data = d
if result is None:continue
score, text = result['score'], result['text']
print(f"【实时结果】:消耗时间:{int((time.time() - start) * 1000)}ms, 识别结果: {text}, 得分: {int(score)}")
# 重置流式识别
predictor.reset_stream()
if __name__ == "__main__":
if args.real_time_demo:
real_time_predict_demo()
else:
if args.is_long_audio:
predict_long_audio()
else:
predict_audio()
此处可能存在不合适展示的内容,页面不予展示。您可通过相关编辑功能自查并修改。
如您确认内容无涉及 不当用语 / 纯广告导流 / 暴力 / 低俗色情 / 侵权 / 盗版 / 虚假 / 无价值内容或违法国家有关法律法规的内容,可点击提交进行申诉,我们将尽快为您处理。